AdaptiGate Autogating

Automatically adjust gates for every file with an algorithm that uses machine learning to learn your gating strategy, for fast, consistent, reproducible results.

Stop redrawing gates for every sample

With AdaptiGate gate positions are automatically applied so you don’t need to redraw gates for every new sample. Use the time you used to spend clicking through polygons for actual analysis.

Adapts to every sample

Machine learning dynamically adjusts gate boundaries to account for sample-to-sample variation, while preserving the gating logic you defined. Your analysis adapts to the data without relying on rigid, identical gate coordinates.

Scale your analysis with confidence

Analyze 10 files or 10,000 without multiplying manual gating work. Apply the same learned strategy across your entire dataset for fast, consistent results.

Keep control over every gate

Review, edit, and understand every automatically generated gate. AdaptiGate reduces manual gating errors while keeping your analysis transparent, reproducible, and under your control.

Why Teams Move Off Manual Gating

Manual Gating

AdaptiGate Autogating

Consistency

Varies by analyst and by day

Same trained strategy applied every time

Speed

Hours per complex panel

Applied automatically across your entire dataset, so you spend more time on analysis and less on clicking

Scaling to new samples

Redo the gating work manually for each new file

Adapts to every sample automatically, without rigid, identical gates

Trust

Depends on documentation discipline

Every gate reviewable, editable, and reproducible

Explore Autogating

Start your Free Trial

Train AdaptiGate on your own gating strategy and see it reproduce your results across a full dataset

How To Use Adaptigate

Learn how to add gating tasks, build gating trees, and run AdaptiGate

Custom Autogating

Custom automated gating pipelines tailored for each client to reproduce existing gating hierarchy

Experience the future of flow cytometry.